Problem analysis method and device based on artificial intelligence, computer equipment and medium

Abstract

The invention belongs to the technical field of artificial intelligence, and relates to an artificial intelligence-based question analysis method, which comprises the following steps of: receiving a legal consultation question input by a user through a preset interactive interface; calling a preset legal knowledge model; performing data analysis on the legal consultation question based on the legal knowledge model to obtain a corresponding data analysis result; generating corresponding suggested text information based on the data analysis result; performing optimization processing on the suggested text information based on a preset optimization strategy to obtain corresponding target suggested text information; and returning the target suggestion text information to the user. The invention further provides a problem analysis device based on artificial intelligence, computer equipment and a storage medium. In addition, the target suggestion text information can be stored in the block chain. The method can be applied to legal affair processing scenes in the fields of financial science and technology, digital medical treatment and the like, and the efficiency and accuracy of legal affair processing are effectively improved through the method.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and can be applied to fields such as fintech and digital healthcare. In particular, it relates to a method, device, computer device, and storage medium for problem analysis based on artificial intelligence. Background Art

[0002] As the scale of various enterprises continues to expand, the business of enterprises has become increasingly complex, which has led to a significant growth trend in the demand for legal consultation within enterprises. In the traditional mode, these legal consultation problems mainly rely on professional lawyer teams to solve. However, this traditional legal consultation method has exposed a series of problems in practical applications.

[0003] First of all, from the perspective of cost, hiring a professional lawyer team is a significant expense for enterprises. The lawyer team needs to pay high salaries, benefits, and training costs, which are a heavy financial burden for most enterprises. Especially in the two highly competitive and relatively low-profit industries of finance and healthcare, cost control is the key to the survival and development of enterprises.

[0004] Secondly, traditional legal consultation also has obvious limitations in terms of processing timeliness. Since lawyer teams usually need to handle a large number of legal matters, when facing urgent or complex legal consultation problems, they often cannot give accurate answers in a timely manner. This lack of timeliness may have an adverse impact on the business decisions of enterprises and even cause enterprises to miss important business opportunities.

[0005] Therefore, the existing processing methods of legal consultation have problems such as high processing costs, low processing efficiency, and low processing accuracy. Summary of the Invention

[0006] The purpose of the embodiments of the present application is to provide a method, device, computer device, and storage medium for problem analysis based on artificial intelligence to solve the technical problems of high processing costs, low processing efficiency, and low processing accuracy existing in the existing processing methods of legal consultation.

[0007] In a first aspect, a method for problem analysis based on artificial intelligence is provided, including:

[0008] Receiving a legal consultation problem input by a user through a preset interaction interface;

[0009] Invoking a preset legal knowledge model;

[0010] Performing data analysis on the legal consultation problem based on the legal knowledge model to obtain a corresponding data analysis result;

[0011] Generating corresponding recommended text information based on the data analysis result;

[0012] Optimize the proposed text information based on a preset optimization strategy to obtain corresponding target proposed text information;

[0013] Return the target proposed text information to the user.

[0014] In a second aspect, a problem analysis device based on artificial intelligence is provided, including:

[0015] A receiving module, configured to receive a legal consultation question input by a user through a preset interaction interface;

[0016] A first calling module, configured to call a preset legal knowledge model;

[0017] An analysis module, configured to perform data analysis on the legal consultation question based on the legal knowledge model to obtain corresponding data analysis results;

[0018] A generating module, configured to generate corresponding proposed text information based on the data analysis results;

[0019] A processing module, configured to optimize the proposed text information based on a preset optimization strategy to obtain corresponding target proposed text information;

[0020] A returning module, configured to return the target proposed text information to the user.

[0021] In a third aspect, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned problem analysis method based on artificial intelligence are implemented.

[0022] In a fourth aspect, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned problem analysis method based on artificial intelligence are implemented.

[0023] In the solution implemented by the above artificial intelligence-based problem analysis method, apparatus, computer device, and storage medium, a legal consultation question input by a user through a preset interaction interface is received; then a preset legal knowledge model is called; then, based on the legal knowledge model, data analysis is performed on the legal consultation question to obtain a corresponding data analysis result; subsequently, corresponding recommended text information is generated based on the data analysis result; further, the recommended text information is optimized based on a preset optimization strategy to obtain corresponding target recommended text information; and finally, the target recommended text information is returned to the user. After receiving the legal consultation question input by the user through the interaction interface, this application will automatically and intelligently perform data analysis on the legal consultation question based on the use of the legal knowledge model to obtain a corresponding data analysis result, generate corresponding recommended text information based on the data analysis result, then optimize the recommended text information based on the use of the optimization strategy to obtain corresponding target recommended text information, and subsequently return the target recommended text information to the user, thereby realizing the provision of an automated legal consultation service for the user based on the use of the legal knowledge model, being able to quickly respond to the legal consultation needs of the user, effectively improving the efficiency and accuracy of legal affairs processing, and reducing the dependence on professional lawyers, greatly reducing the cost of legal affairs processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] To more clearly illustrate the solutions in this application, the following will briefly introduce the drawings required for the description of the embodiments of this application. Obviously, the following-described drawings are some embodiments of this application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0025] Figure 1 FIG. is an exemplary system architecture diagram to which this application can be applied;

[0026] Figure 2 FIG. is a flowchart of an embodiment of the artificial intelligence-based problem analysis method according to this application;

[0027] Figure 3 FIG. is a schematic structural diagram of an embodiment of the artificial intelligence-based problem analysis apparatus according to this application;

[0028] Figure 4 FIG. is a schematic structural diagram of an embodiment of the computer device according to this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0029] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification, claims and drawings of this application are intended to cover non-exclusive inclusion. The terms "first", "second", etc. in the specification, claims or drawings of this application are used to distinguish different objects and not to describe a specific order.

[0030] Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The phrase appearing in various places in the specification is not necessarily referring to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive of other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0031] To enable those skilled in the art to better understand the solution of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0032] As Figure 1 shown, the system architecture 100 may include a terminal device 101, a network 102, and a server 103. The terminal device 101 may be a laptop computer 1011, a tablet computer 1012, or a mobile phone 1013. The network 102 is used as a medium to provide a communication link between the terminal device 101 and the server 103. The network 102 may include various connection types, such as wired, wireless communication links, or fiber optic cables, etc.

[0033] Users can use the terminal device 101 to interact with the server 103 through the network 102 to receive or send messages, etc. Various communication client applications may be installed on the terminal device 101, such as a web browser application, a shopping application, a search application, an instant messaging tool, an email client, a social platform software, etc.

[0034] The terminal device 101 can be various electronic devices with a display screen and supporting web browsing. In addition to the laptop 1011, the tablet computer 1012, or the mobile phone 1013, the terminal device 101 can also be an e-book reader, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 (Moving Picture Experts Group Audio Layer IV) player, a laptop computer, a desktop computer, and so on.

[0035] The server 103 can be a server that provides various services, such as a background server that supports the pages displayed on the terminal device 101.

[0036] It should be noted that the artificial intelligence-based problem analysis method provided by the embodiments of the present application is generally executed by the server / terminal device. Correspondingly, the artificial intelligence-based problem analysis device is generally provided in the server / terminal device.

[0037] It should be understood that Figure 1 the numbers of the terminal devices, the network, and the server in

[0038] Continue to refer to Figure 2 , which shows a flowchart of an embodiment of the artificial intelligence-based problem analysis method according to the present application. According to different requirements, the order of the steps in this flowchart can be changed, and some steps can be omitted. The artificial intelligence-based problem analysis method provided by the embodiments of the present application can be applied to any scenario that requires product recommendation. Then, the artificial intelligence-based problem analysis method can be applied to the products in these scenarios. For example, problem analysis in the financial field or the medical field. The artificial intelligence-based problem analysis method includes the following steps:

[0039] Step S201, receiving a legal consultation question input by the user through a preset interaction interface.

[0040] In this embodiment, the electronic device on which the artificial intelligence-based problem analysis method runs (such as Figure 1The server / terminal device shown can obtain legal consultation questions through wired or wireless connection methods. It should be noted that the above wireless connection methods can include, but are not limited to, 3G / 4G / 5G connections, WiFi connections, Bluetooth connections, WiMAX connections, Zigbee connections, UWB (ultra wideband) connections, and other currently known or future-developed wireless connection methods. The execution entity of this application is a problem analysis system, which can be simply referred to as the system. A user-friendly interaction interface is pre-designed in the system, and users are allowed to input legal consultation questions in the interaction interface through natural language, enabling users to obtain legal problem consultations without professional knowledge. Among them, the layout and style of the user interface can be determined according to actual needs, and interactive elements such as input boxes and buttons are created, and natural language processing (NLP) technology is integrated so that the system can understand the user's input. This application can be applied to legal consultation processing scenarios in the financial and medical fields. Exemplarily, the enterprises to which the system is applied can be financial enterprises and medical enterprises. Financial enterprises can include, for example, insurance enterprises, banks, etc., and medical enterprises can include medical device enterprises, hospitals, etc. The above legal consultation questions can be financial questions or medical questions. Exemplarily, financial questions can include: Whether an individual can claim to waive part of the credit card repayment obligation on the grounds of epidemic prevention and control. Medical questions can include: How a patient should seek legal assistance and compensation due to damages caused by a medical accident.

[0041] Step S202, call a preset legal knowledge model.

[0042] In this embodiment, for the construction process of the above legal knowledge model, this application will further describe the details in subsequent specific embodiments and will not elaborate too much here.

[0043] Step S203, perform data analysis on the legal consultation question based on the legal knowledge model to obtain a corresponding data analysis result.

[0044] In this embodiment, by inputting the legal consultation question into the above legal knowledge model, the legal knowledge model analyzes the legal consultation question to obtain legal knowledge and cases related to the legal consultation question from the legal knowledge model, thereby obtaining the corresponding search result, that is, the above data analysis result.

[0045] Step S204, generate corresponding recommended text information based on the data analysis result.

[0046] In this embodiment, the corresponding key information and logical relationships can be extracted by parsing the above data analysis results. Then, the system automatically assembles the text content of legal suggestions based on the parsed information and uses it as the above-mentioned suggested text information. The text content of the suggested text information may include references to relevant legal provisions, comparative analysis of similar cases, and specific action suggestions. Additionally, during the process of generating the suggested text information, it is ensured that the text content is accurate, complete, and conforms to legal professional terms and norms.

[0047] Step S205: Optimize the suggested text information based on a preset optimization strategy to obtain the corresponding target suggested text information.

[0048] In this embodiment, the specific implementation process of optimizing the suggested text information based on a preset optimization strategy to obtain the corresponding target suggested text information will be further described in detail in subsequent specific embodiments of this application, and will not be elaborated here.

[0049] Step S206: Return the target suggested text information to the user.

[0050] In this embodiment, the specific implementation process of returning the target suggested text information to the user will be further described in detail in subsequent specific embodiments of this application, and will not be elaborated here.

[0051] This application first receives the legal consultation question input by the user through a preset interaction interface; then calls a preset legal knowledge model; then performs data analysis on the legal consultation question based on the legal knowledge model to obtain the corresponding data analysis results; subsequently generates the corresponding suggested text information based on the data analysis results; further optimizes the suggested text information based on a preset optimization strategy to obtain the corresponding target suggested text information; and finally returns the target suggested text information to the user. After receiving the legal consultation question input by the user through the interaction interface, this application will automatically and intelligently perform data analysis on the legal consultation question based on the use of the legal knowledge model to obtain the corresponding data analysis results, generate the corresponding suggested text information based on the data analysis results, then optimize the suggested text information based on the use of the optimization strategy to obtain the corresponding target suggested text information, and subsequently return the target suggested text information to the user, thus realizing the provision of automated legal consultation services for users based on the use of the legal knowledge model, being able to quickly respond to the legal consultation needs of users, effectively improving the efficiency and accuracy of legal affairs processing, and reducing the dependence on professional lawyers, greatly reducing the cost of legal affairs processing.

[0052] In some alternative implementation manners, step S205 includes the following steps:

[0053] Format the recommended text information to obtain the corresponding first text information.

[0054] In this embodiment, by formatting the above-mentioned recommended text information, including paragraph division, title setting, list arrangement, etc., the corresponding first text information is obtained.

[0055] Exemplarily, for legal consultation questions in the financial field: Whether an individual can claim exemption from part of the credit card repayment obligation on the grounds of epidemic prevention and control, and the corresponding recommended text information may include: Generally, with the popularization of electronic payment, the epidemic objectively will not cause the debtor to be unable to perform the obligation of monetary payment in a timely manner, and in principle, the obligation and liability to repay the debt will not be reduced or exempted on the grounds of the epidemic as force majeure. However, if the borrower is unable to repay the debt on time due to objective circumstances such as participating in medical assistance and prevention and control work, which constitutes force majeure, in accordance with the relevant provisions of the Civil Code, the financial institution shall be notified in a timely manner, and the repayment obligation shall be fulfilled in a reasonable period after the relevant circumstances are lifted. At the same time, it is recommended that the borrower actively communicate with the financial institution to understand its relief policies such as loan extension and interest exemption for the epidemic. If the financial institution makes relevant commitments such as debt exemption and repayment extension for relevant debtors affected by the epidemic through notices or announcements, etc., it can be regarded as a change to the contract content.

[0056] And for legal consultation questions in the medical field: How should a patient seek legal assistance and compensation for damages caused by a medical accident, and the corresponding recommended text information may include: Understanding relevant laws and regulations: The patient or the patient's close relatives should understand relevant laws and regulations such as the Civil Code and the Regulations on the Handling of Medical Accidents to clarify their rights and compensation standards. Preserving relevant evidence: To protect their rights and interests, the patient or the patient's close relatives should properly preserve relevant evidence, including medical records, invoices, medical imaging examination materials, etc., which are important bases for medical accident identification and claims. Conducting medical accident identification: The patient can conduct medical accident identification through channels such as negotiating with the hospital, calling the national unified health hotline 12320 to file a complaint, applying for administrative mediation or medical accident technical appraisal with the local health administrative department. The appraisal result will be an important basis for claims. Seeking legal assistance: If the patient is unable to handle the medical accident dispute on their own due to financial difficulties or other reasons, they can apply for legal assistance from the local legal assistance agency. The legal assistance agency will provide free legal consultation and agency services based on the patient's financial situation and the circumstances of the case. Initiating a lawsuit: If the negotiation, mediation or appraisal result fails to meet the reasonable demands of the patient, the patient or their close relatives can file a lawsuit with the people's court, demanding that the medical institution bear the corresponding compensation liability.

[0057] Adjust the layout of the first text information to obtain the corresponding second text information.

[0058] In this embodiment, the above layout adjustment process includes adjusting the typesetting layout according to the text content and length of the above first text information to make it easy to read and understand, so as to obtain the corresponding second text information.

[0059] Perform content review on the second text information based on a preset review policy.

[0060] In this embodiment, for the specific implementation process of performing content review on the second text information based on a preset review policy, this application will further describe the details in subsequent specific embodiments and will not elaborate too much here.

[0061] If the second text information passes the content review, then use the second text information as the target recommended text information.

[0062] This application formats the recommended text information to obtain the corresponding first text information; then performs layout adjustment processing on the first text information to obtain the corresponding second text information; subsequently performs content review on the second text information based on a preset review policy; if the second text information passes the content review, then use the second text information as the target recommended text information. This application formats the recommended text information to obtain the corresponding first text information, performs layout adjustment processing on the first text information to obtain the corresponding second text information, and then performs content review on the second text information based on the use of the review policy, so as to efficiently and intelligently complete the optimization processing of the recommended text information, effectively ensuring that the generated target recommended text information is error-free, logically clear, easy to read and understand, improving the accuracy of the target recommended text information, and improving the user experience.

[0063] In some optional implementation manners of this embodiment, performing content review on the second text information based on a preset review policy includes the following steps:

[0064] Perform grammar and spelling checks on the second text information.

[0065] In this embodiment, the above second text information can be checked sentence by sentence by using a grammar checker and a spelling checker in natural language processing (NLP) technology to identify whether there are grammar errors or spelling errors in the second text information. If it is detected that there are no grammar errors and spelling errors in the second text information, it is determined that the second text information passes the grammar and spelling checks, otherwise it is determined that the second text information fails the grammar and spelling checks. Among them, when it is detected that the second text information fails the grammar and spelling checks, the grammar errors and spelling errors in the second text information can be further corrected.

[0066] If the second text information passes grammar and spelling verification, logical verification is performed on the second text information.

[0067] In this embodiment, the above-mentioned logical verification includes checking whether there are self-contradictions or logical loopholes in the second text information. Specifically, it can automatically examine the logical structure of the second text information to identify whether the logical relationship between various parts of the text is clear and coherent. If it is detected that there are no self-contradictions and no logical loopholes in the second text information, it is determined that the second text information passes the logical verification; otherwise, it is determined that the second text information fails the logical verification.

[0068] If the second text information passes the logical verification, readability verification is performed on the second text information.

[0069] In this embodiment, the above-mentioned readability verification includes evaluating the readability of the second text information to detect whether the second text information is a text with concise language and easy to understand. If it is detected that the second text information is a text with concise language and easy to understand, it is determined that the second text information passes the readability verification; otherwise, it is determined that the second text information fails the readability verification. Among them, if it is detected that the second text information fails the readability verification, the second text information can be further polished and modified appropriately.

[0070] If the second text information passes the readability verification, it is determined that the second text information passes the content review; otherwise, it is determined that the second text information fails the content review.

[0071] In this embodiment, only when it is detected that the second text information passes grammar and spelling verification, logical verification, and readability verification at the same time, it is determined that the second text information passes the content review; otherwise, it is determined that the second text information fails the content review.

[0072] This application performs grammar and spelling verification on the second text information; if the second text information passes grammar and spelling verification, logical verification is performed on the second text information; if the second text information passes logical verification, readability verification is performed on the second text information; if the second text information passes readability verification, it is determined that the second text information passes the content review; otherwise, it is determined that the second text information fails the content review. This application performs grammar and spelling verification, logical verification, and readability verification on the second text information, so that it can automatically and accurately complete the content review process of the second text information, effectively improving the processing accuracy of the content review of the second text information and ensuring the accuracy of the content review result of the obtained second text information.

[0073] In some alternative implementations, step S206 includes the following steps:

[0074] Obtain the preference setting information of the user.

[0075] In this embodiment, the above-mentioned preference setting information refers to the user's preference information for the presentation mode. Among them, when the user registers or uses for the first time, the user's preference for the presentation mode can be asked and recorded to obtain the user's preference setting information. In addition, the user is allowed to change the preference settings at any time during subsequent use. Exemplarily, the presentation mode may include plain text, rich text (including bold, italic, links, etc.), PDF documents, charts, or interactive interfaces (such as question-and-answer forms), etc.

[0076] Determine the corresponding target presentation mode based on the preference setting information.

[0077] In this embodiment, the above-mentioned target presentation mode refers to a presentation mode that matches the above-mentioned preference setting information.

[0078] Perform corresponding adjustment processing on the target recommended text information based on the target presentation mode to obtain the processed specified recommended text information.

[0079] In this embodiment, the format of the above-mentioned target recommended text information can be adjusted according to the determined target presentation mode to generate a text containing the corresponding text format, that is, the above-mentioned specified recommended text information is obtained.

[0080] Perform presentation processing on the specified recommended text information in the interaction interface.

[0081] In this embodiment, the generated specified recommended text information can be presented on the above-mentioned interaction interface so that the user can view and understand it conveniently. Exemplarily, if an interactive interface is selected as the presentation mode, a question-and-answer form interface is implemented. The user can input relevant information through the question prompts on the interface, and the system will generate and update answers in real time according to the user input. Among them, the generated interactive interface is a friendly and easy-to-use interface and provides necessary help and prompt information.

[0082] This application obtains the preference setting information of the user; then determines the corresponding target presentation method based on the preference setting information; then performs corresponding adjustment processing on the target recommended text information based on the target presentation method to obtain the processed specified recommended text information; subsequently, presents the specified recommended text information in the interaction interface. This application obtains the preference setting information of the user, determines the corresponding target presentation method based on the preference setting information, then performs corresponding adjustment processing on the target recommended text information based on the target presentation method to obtain the processed specified recommended text information, and then presents the specified recommended text information in the interaction interface, so that the generated recommended text information can be automatically and intelligently presented to the user in the way preferred by the user, which helps to improve the user satisfaction and the practicality of the system.

[0083] In some alternative implementation manners, before step S202, the above electronic device may further perform the following steps:

[0084] Obtain the pre-collected legal text data.

[0085] In this embodiment, the above legal text data may specifically refer to the legal knowledge base within the enterprise pre-collected by the system administrator, including past legal cases, contract templates, legal opinions, etc. It also includes the sorted legal articles and systems closely related to the enterprise's business, such as industry-specific regulations, legal norms formulated within the company, etc.

[0086] Preprocess the legal text data to obtain the corresponding sample data.

[0087] In this embodiment, for the specific implementation process of preprocessing the legal text data to obtain the corresponding sample data, this application will further describe the details in subsequent specific embodiments and will not elaborate too much here.

[0088] Invoke the preset initial model.

[0089] In this embodiment, there is no specific limitation on the selection of the above initial model, which can be determined according to the actual enterprise needs. For example, the Ollama artificial intelligence large model, simply referred to as the Ollama model, can be used. Among them, the performance and applicability of the Ollama model can be evaluated, and the parameters and configurations of the model can be adjusted according to the enterprise data and needs.

[0090] Train the initial model based on the sample data to obtain the trained first model.

[0091] In this embodiment, the initial model can be trained by using the above sample data. During the training process, the model will learn the legal knowledge and logical relationships in the sample data, and the training process can be monitored to ensure that the legal knowledge and logical relationships learned by the model are accurate, so as to obtain a trained first model.

[0092] Perform fine-tuning on the first model to obtain a corresponding second model.

[0093] In this embodiment, the specific implementation process of performing fine-tuning on the first model to obtain a corresponding second model will be further described in detail in subsequent specific embodiments of this application, and will not be elaborated here too much.

[0094] Use the second model as the legal knowledge model.

[0095] In this embodiment, a dedicated knowledge base can be created in advance to store the generated legal knowledge model, and the security and accessibility of the knowledge base can be ensured. Among them, the knowledge base can be updated and maintained regularly to ensure that the legal knowledge model can call the latest legal knowledge at any time. Specifically, the updates and changes of legal provisions can be monitored, and new legal knowledge can be integrated into the knowledge base. At the same time, the performance of the legal knowledge model can be evaluated regularly, and updated and maintained as needed.

[0096] This application obtains pre-collected legal text data; then preprocesses the legal text data to obtain corresponding sample data; then calls a preset initial model; subsequently trains the initial model based on the sample data to obtain a trained first model; further performs fine-tuning on the first model to obtain a corresponding second model; finally uses the second model as the legal knowledge model. This application preprocesses the pre-collected legal text data to obtain corresponding sample data, then trains the initial model based on the sample data to obtain a trained first model, and then performs fine-tuning on the first model to obtain a corresponding legal knowledge model, so as to efficiently and accurately complete the construction process of the legal knowledge model, improve the construction efficiency of the legal knowledge model, and ensure the model effect of the obtained legal knowledge model.

[0097] In some optional implementation manners of this embodiment, the preprocessing of the legal text data to obtain corresponding sample data includes the following steps:

[0098] Clean the legal text data to obtain corresponding first text data.

[0099] In this embodiment, the above cleaning process refers to removing noise data (such as advertisements, irrelevant information, duplicate content, etc.), correcting error information (such as spelling mistakes, grammar mistakes, etc.), and normalizing the text data. Specifically, text processing tools (such as regular expressions, text editors) can be used to identify and delete noise data in the legal text data. Then, natural language processing (NLP) techniques (such as spell checkers, grammar parsers) are used to correct the error information. Furthermore, the legal text data is standardized, such as unifying fonts, capitalization, punctuation marks, etc., so as to complete the cleaning process of the legal text data and obtain the corresponding first text data.

[0100] Based on a preset text cutting tool, the first text data is cut to obtain the corresponding second text data.

[0101] In this embodiment, the above text cutting tool can specifically adopt a tool based on a cutting algorithm of sentences, paragraphs or keywords. According to the complexity and size of the above first text data, an appropriate cutting block size can be determined, and then the above text cutting tool is used to cut the first text data according to the cutting block size, so as to obtain the corresponding second text data.

[0102] Based on a preset text embedding model, data conversion processing is performed on the second text data to obtain the corresponding vector data.

[0103] In this embodiment, there is no specific limitation on the selection of the above text embedding model, which can be determined according to actual usage requirements. For example, AnythingLLMEmbedder can be adopted. Among them, the performance and applicability of different text embedding models can be evaluated, and a suitable model can be selected as the above text embedding model according to enterprise requirements and data characteristics. Subsequently, the cut second text data is converted into a series of vector data by using the selected text embedding model. And the converted vector data can be stored in a vector database (such as LanceDB) to ensure the security and accessibility of the vector data.

[0104] The vector data is used as the sample data.

[0105] This application performs cleaning processing on the legal text data to obtain corresponding first text data; then performs cutting processing on the first text data based on a preset text cutting tool to obtain corresponding second text data; then performs data conversion processing on the second text data based on a preset text embedding model to obtain corresponding vector data; subsequently, the vector data is used as the sample data. This application obtains the first text data by cleaning the legal text data, and performs cutting processing on the first text data based on the use of the text cutting tool to obtain the second text data, and then performs data conversion processing on the second text data based on the use of the text embedding model to obtain vector data, so as to efficiently and accurately complete the preprocessing of the legal text data and obtain the final sample data, effectively improving the construction efficiency of the sample data and ensuring the data standardization and accuracy of the obtained sample data.

[0106] In some optional implementation manners of this embodiment, the fine-tuning the first model to obtain a corresponding second model includes the following steps:

[0107] Obtain feedback data corresponding to the first model.

[0108] In this embodiment, the use feedback data about the above first model can be collected through various channels such as user satisfaction surveys, online reviews, and customer service records. And ensure that the use feedback data covers different user groups and usage scenarios to comprehensively reflect the model performance. Among them, the collected user feedback data can be further cleaned to remove invalid, duplicate or ambiguous information, and the user feedback data can be sorted out, classified into positive feedback, negative feedback and neutral feedback, and key information points can be extracted to obtain the corresponding feedback data.

[0109] Analyze and process the feedback data to obtain corresponding analysis results.

[0110] In this embodiment, the problems and deficiencies of the model can be identified by deeply analyzing the negative feedback in the above feedback data. And analyze the positive feedback in the above feedback data to understand the reasons for user satisfaction and the advantages of the model. And comprehensively consider the neutral feedback in the above feedback data to obtain the objective evaluation and improvement suggestions of users on the model, so as to generate corresponding analysis results.

[0111] Construct a corresponding fine-tuning strategy based on the analysis results.

[0112] In this embodiment, specific fine-tuning objectives can be set according to the analysis results, such as improving the accuracy of legal advice, enhancing the model response speed, etc. Then, corresponding fine-tuning strategies can be formulated for the fine-tuning objectives. For example, for improving the accuracy of legal advice, optimizing the legal knowledge model, increasing training data, etc. can be considered; for enhancing the model response speed, optimizing the algorithm, upgrading the hardware, etc. can be done.

[0113] The first model is fine-tuned based on the fine-tuning strategy to obtain a corresponding third model.

[0114] In this embodiment, the process of fine-tuning the first model based on the fine-tuning strategy includes: preparing corresponding training data and test data according to the fine-tuning strategy. For example, if the legal knowledge model needs to be optimized, more legal provisions, cases, and solutions need to be collected as training data. Then, the first model is adjusted and optimized. Specifically, it includes adjusting model parameters, improving the algorithm structure, adding feature engineering, etc., and training the model with the new training data while monitoring the performance changes of the model. After that, testing is performed on the adjusted third model, and the adjusted third model is evaluated using the test data to compare the performance differences before and after fine-tuning to verify whether the fine-tuning effect meets the expected goal.

[0115] The third model is used as the second model.

[0116] In this embodiment, if it is detected that the fine-tuning effect of the third model meets the expected goal, it is determined that the fine-tuning process of the first model is completed, and the third model is used as the above-mentioned second model. In addition, a long-term user feedback collection and analysis mechanism can be established to ensure that the legal knowledge model can continuously adapt to the changes in user needs. And regularly review and analyze the user feedback data to provide strong support for the continuous optimization of the legal knowledge model.

[0117] This application obtains feedback data corresponding to the first model; then analyzes and processes the feedback data to obtain corresponding analysis results; then constructs a corresponding fine-tuning strategy based on the analysis results; subsequently, fine-tunes the first model based on the fine-tuning strategy to obtain a corresponding third model, and uses the third model as the second model. This application obtains feedback data corresponding to the first model, analyzes and processes the feedback data to obtain corresponding analysis results, then constructs a corresponding fine-tuning strategy based on the analysis results, and further completes the fine-tuning process of the first model based on the use of the fine-tuning strategy, so as to automatically and intelligently fine-tune the first model according to user feedback, continuously improve the accuracy of the legal knowledge model and user satisfaction, which is beneficial to enhancing the competitiveness of the legal knowledge model and improving the user experience.

[0118] In some alternative implementations, the obtained user information has obtained the user's consent and complies with the provisions of relevant laws and relevant policies.

[0119] In addition, the non-company software tools or components that appear in the embodiments of the present application are only introduced by way of example and do not represent actual use.

[0120] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not imply the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0121] It should be emphasized that to further ensure the privacy and security of the above-mentioned target recommended text information, the above-mentioned target recommended text information can also be stored in a node of a blockchain.

[0122] The blockchain referred to in the present application is a new application mode of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, and encryption algorithm. A blockchain, in essence, is a decentralized database, a series of data blocks generated by using cryptographic methods, and each data block contains information about a batch of network transactions, which is used to verify the validity of the information (anti-counterfeiting) and generate the next block. A blockchain can include a blockchain underlying platform, a platform product service layer, an application service layer, etc.

[0123] The embodiments of the present application can obtain and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is to use a digital computer or a machine controlled by a digital computer to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to obtain the best results of theory, methods, technologies, and application systems.

[0124] Artificial intelligence basic technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, and mechatronics. Artificial intelligence software technologies mainly include several major directions such as computer vision technology, robotics, biometric technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0125] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through computer-readable instructions. These computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, an optical disc, a Read-Only Memory (ROM), or a Random Access Memory (RAM), etc.

[0126] It should be understood that although the steps in the flowchart of the accompanying drawings are shown in sequence according to the indication of the arrows, these steps do not necessarily have to be executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps does not have a strict order limit, and they can be executed in other orders. Moreover, at least some of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages. These sub-steps or stages do not necessarily have to be executed at the same moment, but can be executed at different moments. Their execution order does not necessarily have to be sequential, but can be executed alternately or in turn with at least some of the other steps or sub-steps or stages of the other steps.

[0127] Further referring to Figure 3 As an implementation of the method shown above Figure 2 In an embodiment of a problem analysis device based on artificial intelligence provided by the present application, this device embodiment corresponds to the method embodiment shown in Figure 2 and can be specifically applied to various electronic devices.

[0128] As shown in Figure 3 The problem analysis device 300 based on artificial intelligence described in this embodiment includes: a receiving module 301, a first calling module 302, an analysis module 303, a generating module 304, a processing module 305, and a returning module 306. Among them:

[0129] The receiving module 301 is configured to receive a legal consultation question input by a user through a preset interaction interface;

[0130] The first calling module 302 is configured to call a preset legal knowledge model;

[0131] The analysis module 303 is configured to perform data analysis on the legal consultation question based on the legal knowledge model to obtain a corresponding data analysis result;

[0132] The generating module 304 is configured to generate corresponding recommended text information based on the data analysis result;

[0133] A processing module 305, configured to optimize the recommended text information based on a preset optimization strategy to obtain corresponding target recommended text information;

[0134] A return module 306, configured to return the target recommended text information to the user.

[0135] In this embodiment, the operations respectively performed by the above modules or units correspond one by one to the steps of the artificial intelligence-based problem analysis method in the foregoing embodiment, and will not be elaborated herein.

[0136] In some optional implementation manners of this embodiment, the processing module 305 includes:

[0137] A first processing sub-module, configured to format the recommended text information to obtain corresponding first text information;

[0138] A second processing sub-module, configured to adjust the layout of the first text information to obtain corresponding second text information;

[0139] An auditing sub-module, configured to perform content auditing on the second text information based on a preset auditing strategy;

[0140] A first determination sub-module, configured to use the second text information as the target recommended text information if the second text information passes the content audit.

[0141] In this embodiment, the operations respectively performed by the above modules or units correspond one by one to the steps of the artificial intelligence-based problem analysis method in the foregoing embodiment, and will not be elaborated herein.

[0142] In some optional implementation manners of this embodiment, the auditing sub-module includes:

[0143] A first verification unit, configured to perform grammar and spelling verification on the second text information;

[0144] A second verification unit, configured to perform logic verification on the second text information if the second text information passes the grammar and spelling verification;

[0145] A third verification unit, configured to perform readability verification on the second text information if the second text information passes the logic verification;

[0146] A determination unit, configured to determine that the second text information passes the content audit if the second text information passes the readability verification, otherwise determine that the second text information fails the content audit.

[0147] In this embodiment, the operations respectively performed by the above modules or units correspond one by one to the steps of the artificial intelligence-based problem analysis method in the foregoing embodiment, and will not be elaborated herein.

[0148] In some alternative implementation manners of this embodiment, the return module 306 includes:

[0149] A first acquisition sub-module, configured to acquire the preference setting information of the user;

[0150] A second determination sub-module, configured to determine a corresponding target presentation manner based on the preference setting information;

[0151] An adjustment sub-module, configured to perform corresponding adjustment processing on the target recommended text information based on the target presentation manner to obtain processed specified recommended text information;

[0152] A presentation sub-module, configured to perform presentation processing on the specified recommended text information in the interaction interface.

[0153] In this embodiment, the operations respectively performed by the above modules or units correspond one by one to the steps of the artificial intelligence-based problem analysis method in the foregoing embodiment, and will not be elaborated herein.

[0154] In some alternative implementation manners of this embodiment, the artificial intelligence-based problem analysis device further includes:

[0155] An acquisition module, configured to acquire pre-collected legal text data;

[0156] A preprocessing module, configured to preprocess the legal text data to obtain corresponding sample data;

[0157] A second calling module, configured to call a preset initial model;

[0158] A training module, configured to train the initial model based on the sample data to obtain a trained first model;

[0159] A fine-tuning module, configured to perform fine-tuning processing on the first model to obtain a corresponding second model;

[0160] A determination module, configured to use the second model as the legal knowledge model.

[0161] In this embodiment, the operations respectively performed by the above modules or units correspond one by one to the steps of the artificial intelligence-based problem analysis method in the foregoing embodiment, and will not be elaborated herein.

[0162] In some alternative implementation manners of this embodiment, the preprocessing module includes:

[0163] A cleaning sub-module for cleaning the legal text data to obtain corresponding first text data;

[0164] A cutting sub-module for cutting the first text data based on a preset text cutting tool to obtain corresponding second text data;

[0165] A conversion sub-module for performing data conversion processing on the second text data based on a preset text embedding model to obtain corresponding vector data;

[0166] A third determination sub-module for using the vector data as the sample data.

[0167] In this embodiment, the operations respectively performed by the above modules or units correspond one by one to the steps of the artificial intelligence-based problem analysis method in the foregoing embodiment, and will not be elaborated herein.

[0168] In some optional implementation manners of this embodiment, the fine-tuning module includes:

[0169] A second acquisition sub-module for acquiring feedback data corresponding to the first model;

[0170] An analysis sub-module for analyzing and processing the feedback data to obtain corresponding analysis results;

[0171] A construction sub-module for constructing a corresponding fine-tuning strategy based on the analysis results;

[0172] A fine-tuning sub-module for fine-tuning the first model based on the fine-tuning strategy to obtain a corresponding third model;

[0173] A fourth determination sub-module for using the third model as the second model.

[0174] In this embodiment, the operations respectively performed by the above modules or units correspond one by one to the steps of the artificial intelligence-based problem analysis method in the foregoing embodiment, and will not be elaborated herein.

[0175] To solve the above technical problems, an embodiment of the present application further provides a computer device. For details, please refer to Figure 4 , Figure 4 which is the basic structural block diagram of the computer device in this embodiment.

[0176] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are communicatively connected to each other via a system bus. It should be noted that only the computer device 4 with components 41-43 is shown in the figure, but it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively. Among them, those skilled in the art of the present technology can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0177] The computer device can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The computer device can perform human-computer interaction with the user through means such as a keyboard, a mouse, a remote control, a touchpad, or a voice control device.

[0178] The memory 41 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, a hard disk, a multimedia card, a card-type memory (such as an SD or DX memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 41 can be an internal storage unit of the computer device 4, such as the hard disk or memory of the computer device 4. In other embodiments, the memory 41 can also be an external storage device of the computer device 4, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the computer device 4. Of course, the memory 41 can also include both the internal storage unit and the external storage device of the computer device 4. In this embodiment, the memory 41 is generally used to store the operating system and various application software installed on the computer device 4, such as computer-readable instructions for the problem analysis method based on artificial intelligence. In addition, the memory 41 can also be used to temporarily store various data that have been output or will be output.

[0179] In some embodiments, the processor 42 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips. The processor 42 is generally used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to run the computer-readable instructions stored in the memory 41 or process data, such as running the computer-readable instructions of the artificial intelligence-based problem analysis method.

[0180] The network interface 43 may include a wireless network interface or a wired network interface. The network interface 43 is generally used to establish a communication connection between the computer device 4 and other electronic devices.

[0181] Compared with the prior art, the embodiments of the present application mainly have the following beneficial effects:

[0182] In the embodiments of the present application, by receiving the legal consultation questions input by the user through a preset interaction interface; then calling a preset legal knowledge model; then performing data analysis on the legal consultation questions based on the legal knowledge model to obtain corresponding data analysis results; subsequently generating corresponding recommended text information based on the data analysis results; further optimizing the recommended text information based on a preset optimization strategy to obtain corresponding target recommended text information; and finally returning the target recommended text information to the user. After receiving the legal consultation questions input by the user through the interaction interface, the present application will automatically and intelligently perform data analysis on the legal consultation questions based on the use of the legal knowledge model to obtain corresponding data analysis results, generate corresponding recommended text information based on the data analysis results, then optimize the recommended text information based on the use of the optimization strategy to obtain corresponding target recommended text information, and subsequently return the target recommended text information to the user, thereby realizing the provision of an automated legal consultation service for users based on the use of the legal knowledge model, being able to quickly respond to the legal consultation needs of users, effectively improving the efficiency and accuracy of legal affairs processing, reducing the dependence on professional lawyers, and greatly reducing the cost of legal affairs processing.

[0183] The present application also provides another implementation manner, that is, to provide a computer-readable storage medium storing computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to execute the steps of the artificial intelligence-based problem analysis method as described above.

[0184] Compared with the prior art, the embodiments of the present application mainly have the following beneficial effects:

[0185] In the embodiments of the present application, by receiving a legal consultation question input by a user through a preset interaction interface; then calling a preset legal knowledge model; then performing data analysis on the legal consultation question based on the legal knowledge model to obtain a corresponding data analysis result; subsequently generating corresponding recommended text information based on the data analysis result; further optimizing the recommended text information based on a preset optimization strategy to obtain corresponding target recommended text information; and finally returning the target recommended text information to the user. After receiving the legal consultation question input by the user through the interaction interface, the present application will automatically and intelligently perform data analysis on the legal consultation question based on the use of the legal knowledge model to obtain a corresponding data analysis result, generate corresponding recommended text information based on the data analysis result, then optimize the recommended text information based on the use of the optimization strategy to obtain corresponding target recommended text information, and subsequently return the target recommended text information to the user, thereby realizing the provision of an automated legal consultation service for users based on the use of the legal knowledge model, being able to quickly respond to the legal consultation needs of users, effectively improving the efficiency and accuracy of legal affairs processing, and reducing the dependence on professional lawyers, greatly reducing the cost of legal affairs processing.

[0186] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disc) and includes several instructions for causing a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present application.

[0187] Obviously, the above-described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The accompanying drawings show the preferred embodiments of the present application, but do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosed content of the present application more thorough and comprehensive. Although the present application has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions described in the foregoing specific embodiments, or perform equivalent replacements on some of the technical features. Any equivalent structure directly or indirectly using the content of the specification and drawings of the present application in other related technical fields is equally within the scope of the patent protection of the present application.

Claims

1. An artificial intelligence-based problem analysis method, characterized in that It includes the following steps: Receive a legal consultation question input by the user through a preset interaction interface; Invoke a preset legal knowledge model; Perform data analysis on the legal consultation question based on the legal knowledge model to obtain a corresponding data analysis result; Generate corresponding recommended text information based on the data analysis result; Optimize the recommended text information based on a preset optimization strategy to obtain corresponding target recommended text information; Return the target recommended text information to the user.

2. The problem analysis method based on artificial intelligence according to claim 1, wherein The step of optimizing the recommended text information based on a preset optimization strategy to obtain corresponding target recommended text information specifically includes: Format the recommended text information to obtain corresponding first text information; Adjust the layout of the first text information to obtain corresponding second text information; Conduct content review on the second text information based on a preset review strategy; If the second text information passes the content review, use the second text information as the target recommended text information.

3. The problem analysis method based on artificial intelligence according to claim 2, wherein The step of conducting content review on the second text information based on a preset review strategy specifically includes: Check the grammar and spelling of the second text information; If the second text information passes the grammar and spelling check, conduct a logic check on the second text information; If the second text information passes the logic check, conduct a readability check on the second text information; If the second text information passes the readability check, determine that the second text information passes the content review, otherwise determine that the second text information fails the content review.

4. The method for problem analysis based on artificial intelligence according to claim 1, wherein The step of returning the target recommended text information to the user specifically includes: Obtain the user's preference setting information; Determine a corresponding target presentation method based on the preference setting information; Perform corresponding adjustment processing on the target recommended text information based on the target presentation method to obtain processed designated recommended text information; Present the designated recommended text information in the interaction interface.

5. The problem analysis method based on artificial intelligence according to claim 1, wherein Before the step of invoking the preset legal knowledge model, it further includes: Obtain pre-collected legal text data; Preprocess the legal text data to obtain corresponding sample data; Invoke a preset initial model; Train the initial model based on the sample data to obtain a trained first model; Fine-tune the first model to obtain a corresponding second model; Use the second model as the legal knowledge model.

6. The method for problem analysis based on artificial intelligence according to claim 5, wherein The step of preprocessing the legal text data to obtain corresponding sample data specifically includes: Clean the legal text data to obtain corresponding first text data; Perform cutting processing on the first text data based on a preset text cutting tool to obtain corresponding second text data; Perform data conversion processing on the second text data based on a preset text embedding model to obtain corresponding vector data; Use the vector data as the sample data.

7. The method for problem analysis based on artificial intelligence according to claim 5, wherein The step of fine-tuning the first model to obtain a corresponding second model specifically includes: Obtain feedback data corresponding to the first model; Analyze and process the feedback data to obtain corresponding analysis results; Construct a corresponding fine-tuning strategy based on the analysis results; Fine-tune the first model based on the fine-tuning strategy to obtain a corresponding third model; Use the third model as the second model.

8. An artificial intelligence-based problem analysis device, characterized in that, Comprising: A receiving module, configured to receive a legal consultation question input by a user through a preset interaction interface; A first calling module, configured to call a preset legal knowledge model; An analysis module, configured to perform data analysis on the legal consultation question based on the legal knowledge model to obtain corresponding data analysis results; A generating module, configured to generate corresponding recommended text information based on the data analysis results; A processing module, configured to optimize the recommended text information based on a preset optimization strategy to obtain corresponding target recommended text information; A returning module, configured to return the target recommended text information to the user.

9. A computer device, characterized in that, Comprising a memory and a processor, wherein computer-readable instructions are stored in the memory, and when the processor executes the computer-readable instructions, the steps of the artificial intelligence-based problem analysis method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that, Computer-readable instructions are stored on the computer-readable storage medium, and when the computer-readable instructions are executed by a processor, the steps of the artificial intelligence-based problem analysis method according to any one of claims 1 to 7 are implemented.

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